This section is intended to introduce various aspects of the art, which may be associated with exemplary embodiments of the present techniques. This discussion is believed to assist in providing a framework to facilitate a better understanding of particular aspects of the present techniques. Accordingly, it should be understood that this section should be read in this light, and not necessarily as admissions of prior art.
To search for hydrocarbon accumulations in the earth, geoscientists often use methods of remote sensing to look below the earth's surface. In the routinely used seismic reflection method, man-made sound waves are generated near the surface. The sound propagates into the earth, and as the sound passes from one rock layer into another, a small portion of the sound reflects back to the surface, where it is recorded as seismic data. Typically, hundreds to thousands of recording instruments are employed. Sound waves are sequentially excited at many different surface locations, and the recording instruments record the sound waves as seismic data. A two-dimensional or three-dimensional image of the subsurface is obtained from data processing of the recorded seismic data. However, such two-dimensional or three-dimensional images may occupy a large amount of storage space, for example, ranging from hundreds of megabytes to many gigabytes of storage space.
In current practice, an interpreter is initially tasked with scoping the data to identify regions in the subsurface with the potential of containing hydrocarbon accumulations. These regions are then carefully examined to develop a list of prospects, or areas in which hydrocarbons are predicted to exist in economic quantities. As used herein, the term “prospect” refers to a geologic or geophysical anomalous feature that is recommended for drilling a well based on direct hydrocarbon indications or a reasonable probability of encountering reservoir-quality rocks, a trap of sufficient size, adequate sealing rocks, or appropriate conditions for generation and migration of hydrocarbons to fill the trap. Current techniques for seismic data analysis, however, are often tedious, labor-intensive, and time-consuming.
Seismic interpretation generally involves a person skilled in geologic interpretation, referred to as an interpreter, who reviews seismic reflections and maps the seismic reflections into seismic horizons. A seismic horizon may include boundaries in the subsurface structures that are useful to an interpreter, which is a subjective process. Further, manually identifying seismic horizons using an interpreter may be a time consuming process.
Geological and geophysical features exist at many different scales. Subsurface channels may exhibit widths ranging from tens of meters (m) to tens of kilometers (km). Seismic images of the subsurface are formed by sampling, and, thus, the spatial and temporal sampling intervals used for data acquisition and processing affect the scale of features in terms of the number of samples. In addition, interpreters often reduce the amount of seismic data that is stored to decrease storage costs or to increase computational efficiency. For example, interpreters may remove every other sample within the seismic data prior to storage. On the other hand, additional samples within the seismic data may be interpolated for computation or visualization purposes, resulting in an increase in storage costs and a decrease in computational efficiency.
Tool sets for computer-aided volume interpretation typically include horizon tracking techniques that are used to find seismic horizons. Horizon tracking may follow the peaks of seismic amplitudes starting with a user provided seed point in a vertical seismic section. The vertical seismic section can be either a cross-line vertical section in the y-z plane or an in-line vertical section in the x-z plane.
Another horizon tracking technique is known as “seed detection,” which is a technique for growing a region in a three dimensional seismic data volume. Seed detection may result in a set of connected voxels in a 3D seismic data volume which fulfill user-specified attribute criteria. Seed detection may begin with a point in a data volume to connect with admissible neighbors to fully define a connected object. Admissible neighbors are those surrounding points that meet user defined criteria. The new points are added to the current object and the procedure continues until it reaches a point where no further admissible neighbors exist.
An example of a horizon tracking technique is discussed in United States Patent Application Publication No. 2008/0285384 by James. The application describes a seed picking algorithm that can use a first point for picking a set of second points from a data set. Each of the points in the set of second points can be set as the first point, and the algorithm may repeat. An iteration number or other attribute can be assigned to the points, and the iteration number can correspond to the number of times the algorithm has been repeated to process the point. The attribute or a number of attributes can be displayed as a visual characteristic for each point. An iterative process can be applied to a set of seismic data points, starting at a seed data point and finding a set of next iteration seed points from the set of points neighboring the seed point, continuing only with next iteration seed points. The number of points that are found by the process when the point is used as a seed data point can be recorded for each of a set of data points.
In another example, International Patent Application Publication No. 2010/047856 by Mark Dobin, et al., describes a method and system that may identify a geologic object through cross sections of a geologic data volume. The method includes obtaining a geologic data volume having a set of cross sections. Then, two or more cross sections can be selected, and a transformation vector can be estimated between the cross sections. Based on the transformation vector, a geologic object can be identified within the geologic data volume.
In still another example, United States Patent Application Publication No. 2008/0071477 by Li, et al., describes a method that may determine a fault surface in a formation by determining a first plurality of cross correlation values for a 3D Volume associated, respectively, with a corresponding first number of 3D Volumes. A first minimum one of a first number of cross correlation values can be selected. Additionally, a first derived fault segment corresponding to the first minimum of the first number of cross correlation values can be selected, including the first derived fault segment approximately lying on the fault surface and tending to determine the fault surface in the formation.
The existing techniques described above tend to find geologic objects, including horizons, using input from an interpreter. However, such techniques may be labor intensive and time consuming due to the dependency on such input from the interpreter. Therefore, such techniques may not be cost-effective for very large seismic data sets.